Taking Quantitative Genomics into the Wild

A key goal in studies of ecology and evolution is understanding the causes of phenotypic diversity in nature. Most traits of interest, such as those relating to morphology, life-history, immunity and behaviour are quantitative, and phenotypic variation is driven by the cumulative effects of genetic and environmental variation. The field of quantitative genetics aims to quantify the additive genetic component of this trait variance (i.e. the "heritability"), often with the underlying assumption that trait variance is driven by many loci of infinitesimal effects throughout the genome. This approach allows us to understand the evolutionary potential of natural populations and can be extended to examine the genetic covariation with fitness to predict responses to selection. Therefore, quantitative genetic studies are fundamental to understanding evolution in the wild. Over the last two decades, there has been a wealth of studies investigating trait heritabilities and genetic correlations, but these were initially limited to long-term studies of pedigreed populations or common-garden experiments. However, genomic technologies have since allowed quantitative genetic studies in a more diverse range of wild systems and has increased the opportunities for addressing outstanding questions in ecology and evolution. In particular, genomic studies can uncover the genetic basis of fitness-related quantitative traits, allowing a better understanding of their evolutionary dynamics. We organised this special issue to highlight new work and review recent advances at the cutting edge of "Wild Quantitative Genomics". In this Editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.Comment: 17 page (plus references) Editorial for a special issue of Proceedings of the Royal Society B: Biological Sciences. Revised submissio

The relationship between body positioning, muscle activity, and spinal kinematics in cyclists with and without low back pain

Objectives: To determine if relationships exist between body positioning, spinal kinematics, and muscle activity in active cyclists with non-traumatic LBP. To explore variations in optimal positioning and bike set up in order to address variables associated with LBP in the physical therapy clinic.https://jdc.jefferson.edu/dptcapstones/1003/thumbnail.jp

The observable properties of galaxy accretion events in Milky Way-like galaxies in the FIRE-2 cosmological simulations

In the $\Lambda$-Cold Dark Matter model of the Universe, galaxies form in part through accreting satellite systems. Previous work have built an understanding of the signatures of these processes contained within galactic stellar halos. This work revisits that picture using seven Milky Way-like galaxies in the \textit{Latte} suite of FIRE-2 cosmological simulations. The resolution of these simulations allows a comparison of contributions from satellites above M$_{*}$$\gtrsim$10$\times$$^{7}$M$_{\odot}$, enabling the analysis of observable properties for disrupted satellites in a fully self-consistent and cosmological context. Our results show that, the time of accretion and the stellar mass of an accreted satellite are fundamental parameters that in partnership dictate the resulting spatial distribution, orbital energy, and [$\alpha$/Fe]-[Fe/H] compositions of the stellar debris of such mergers $at$ $present$ $day$. These parameters also govern the resulting dynamical state of an accreted galaxy at $z=0$, leading to the expectation that the inner regions of the stellar halo (R$_{\mathrm{GC}}$ $\lesssim$30 kpc) should contain fully phase-mixed debris from both lower and higher mass satellites. In addition, we find that a significant fraction of the lower mass satellites accreted at early times deposit debris in the outer halo (R$_{\mathrm{GC}}$ $>$50 kpc) that are $not$ fully phased-mixed, indicating that they could be identified in kinematic surveys. Our results suggest that, as future surveys become increasingly able to map the outer halo of our Galaxy, they may reveal the remnants of long-dead dwarf galaxies whose counterparts are too faint to be seen $in$ $situ$ in higher redshift surveys.Comment: Submitted for publication in Ap

On the co-rotation of Milky Way satellites: LMC-mass satellites induce apparent motions in outer halo tracers

Understanding the physical mechanism behind the formation of a co-rotating thin plane of satellite galaxies, like the one observed around the Milky Way (MW), has been challenging. The perturbations induced by a massive satellite galaxy, like the Large Magellanic Cloud (LMC) provide valuable insight into this problem. The LMC induces an apparent co-rotating motion in the outer halo by displacing the inner regions of the halo with respect to the outer halo. Using the Latte suite of FIRE-2 cosmological simulations of MW-mass galaxies, we confirm that the apparent motion of the outer halo induced by the infall of a massive satellite changes the observed distribution of orbital poles of outer-halo tracers, including satellites. We quantify the changes in the distribution of orbital poles using the two-point angular correlation function and find that all satellites induce changes. However, the most massive satellites with pericentric passages between 30-100kpc induce the largest changes. The best LMC-like satellite analog shows the largest change in orbital pole distribution. The dispersion of orbital poles decreases by 20{\deg} during the first two pericentric passages. Even when excluding the satellites brought in with the LMC-like satellite, there is clustering of orbital poles. These results suggest that in the MW, the recent pericentric passage of the LMC should have changed the observed distribution of orbital poles of all other satellites. Therefore, studies of kinematically-coherent planes of satellites that seek to place the MW in a cosmological context should account for the existence of a massive satellite like the LMC.Comment: 20 pages, 10 figures. ApJ submitted, Comments are welcom

Photometric Redshifts of Quasars

We demonstrate that the design of the Sloan Digital Sky Survey (SDSS) filter system and the quality of the SDSS imaging data are sufficient for determining accurate and precise photometric redshifts (``photo-z''s) of quasars. Using a sample of 2625 quasars, we show that photo-z determination is even possible for z<=2.2 despite the lack of a strong continuum break that robust photo-z techniques normally require. We find that, using our empirical method on our sample of objects known to be quasars, approximately 70% of the photometric redshifts are correct to within delta z = 0.2; the fraction of correct photometric redshifts is even better for z>3. The accuracy of quasar photometric redshifts does not appear to be dependent upon magnitude to nearly 21st magnitude in i'. Careful calibration of the color-redshift relation to 21st magnitude may allow for the discovery of on the order of 10^6 quasars candidates in addition to the 10^5 quasars that the SDSS will confirm spectroscopically. We discuss the efficient selection of quasar candidates from imaging data for use with the photometric redshift technique and the potential scientific uses of a large sample of quasar candidates with photometric redshifts.Comment: 29 pages, 8 figures, submitted to A

Real-time pandemic surveillance using hospital admissions and mobility data

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and health-care demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95%CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.This work was supported by Grant U01IP001136 from the CDC, Grant NIH R01 AI151176 from the NIH, and a generous donation from Tito’s Handmade Vodka.StatisticsIntegrative BiologyOperations Research and Industrial EngineeringTexas Advanced Computing Center (TACC)Dell Medical SchoolInformation, Risk, and Operations Management (IROM

Views of Non-Physician Stakeholders on Barriers &amp; Facilitators to AYA Cancer Care in Latin America

Cancer is the fourth leading cause of death in adolescent and young adults (AYA: ages15-39) worldwide. Over 90% of AYAs with cancer live in low-and-middle-incomecountries (LMIC). Little is known about thecurrent landscape of AYA oncology care in these settings. Guidelines for the care of thisunique population in LMIC in Latin America are lacking

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio